Overview

Dataset statistics

Number of variables25
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory203.9 B

Variable types

Numeric23
Categorical2

Alerts

Año is highly overall correlated with Accesos por cada 100 hogares and 20 other fieldsHigh correlation
Accesos por cada 100 hogares is highly overall correlated with Año and 20 other fieldsHigh correlation
Accesos por cada 100 hab is highly overall correlated with Año and 20 other fieldsHigh correlation
Banda ancha fija is highly overall correlated with Año and 20 other fieldsHigh correlation
Dial up is highly overall correlated with Año and 17 other fieldsHigh correlation
Total_BA is highly overall correlated with Año and 20 other fieldsHigh correlation
ADSL is highly overall correlated with Año and 20 other fieldsHigh correlation
Cablemodem is highly overall correlated with Año and 20 other fieldsHigh correlation
Fibra óptica is highly overall correlated with Año and 20 other fieldsHigh correlation
Wireless is highly overall correlated with Año and 20 other fieldsHigh correlation
Otros_tecno is highly overall correlated with Año and 18 other fieldsHigh correlation
Total_tecno is highly overall correlated with Año and 20 other fieldsHigh correlation
Mbps (Media de bajada) is highly overall correlated with Año and 20 other fieldsHigh correlation
Hasta 512 kbps is highly overall correlated with Otros_tecno and 1 other fieldsHigh correlation
Entre 512 Kbps y 1 Mbps is highly overall correlated with Año and 20 other fieldsHigh correlation
Entre 1 Mbps y 6 Mbps is highly overall correlated with Año and 20 other fieldsHigh correlation
Entre 6 Mbps y 10 Mbps is highly overall correlated with Año and 19 other fieldsHigh correlation
Entre 10 Mbps y 20 Mbps is highly overall correlated with Entre 20 Mbps y 30 Mbps and 1 other fieldsHigh correlation
Entre 20 Mbps y 30 Mbps is highly overall correlated with Año and 19 other fieldsHigh correlation
Más de 30 Mbps is highly overall correlated with Año and 20 other fieldsHigh correlation
Otros_velo_rango is highly overall correlated with Año and 19 other fieldsHigh correlation
Total_velo_rango is highly overall correlated with Año and 20 other fieldsHigh correlation
Ingresos (miles de pesos) is highly overall correlated with Año and 20 other fieldsHigh correlation
Trimestre is highly overall correlated with PeriodoHigh correlation
Periodo is highly overall correlated with Año and 23 other fieldsHigh correlation
Periodo is uniformly distributedUniform
Periodo has unique valuesUnique
Accesos por cada 100 hogares has unique valuesUnique
Accesos por cada 100 hab has unique valuesUnique
Banda ancha fija has unique valuesUnique
Total_BA has unique valuesUnique
ADSL has unique valuesUnique
Cablemodem has unique valuesUnique
Fibra óptica has unique valuesUnique
Wireless has unique valuesUnique
Otros_tecno has unique valuesUnique
Total_tecno has unique valuesUnique
Mbps (Media de bajada) has unique valuesUnique
Entre 512 Kbps y 1 Mbps has unique valuesUnique
Entre 1 Mbps y 6 Mbps has unique valuesUnique
Entre 6 Mbps y 10 Mbps has unique valuesUnique
Entre 10 Mbps y 20 Mbps has unique valuesUnique
Entre 20 Mbps y 30 Mbps has unique valuesUnique
Más de 30 Mbps has unique valuesUnique
Total_velo_rango has unique valuesUnique
Ingresos (miles de pesos) has unique valuesUnique
Otros_velo_rango has 15 (45.5%) zerosZeros

Reproduction

Analysis started2023-07-16 22:37:44.160081
Analysis finished2023-07-16 22:39:09.798975
Duration1 minute and 25.64 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Año
Real number (ℝ)

Distinct9
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.6364
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:09.879368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2018
Q32020
95-th percentile2021
Maximum2022
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4214947
Coefficient of variation (CV)0.0012001641
Kurtosis-1.1810557
Mean2017.6364
Median Absolute Deviation (MAD)2
Skewness0.033458779
Sum66582
Variance5.8636364
MonotonicityDecreasing
2023-07-16T17:39:10.030859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2021 4
12.1%
2020 4
12.1%
2019 4
12.1%
2018 4
12.1%
2017 4
12.1%
2016 4
12.1%
2015 4
12.1%
2014 4
12.1%
2022 1
 
3.0%
ValueCountFrequency (%)
2014 4
12.1%
2015 4
12.1%
2016 4
12.1%
2017 4
12.1%
2018 4
12.1%
2019 4
12.1%
2020 4
12.1%
2021 4
12.1%
2022 1
 
3.0%
ValueCountFrequency (%)
2022 1
 
3.0%
2021 4
12.1%
2020 4
12.1%
2019 4
12.1%
2018 4
12.1%
2017 4
12.1%
2016 4
12.1%
2015 4
12.1%
2014 4
12.1%

Trimestre
Categorical

Distinct4
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size392.0 B
1
4
3
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 9
27.3%
4 8
24.2%
3 8
24.2%
2 8
24.2%

Length

2023-07-16T17:39:10.343370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T17:39:10.514365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9
27.3%
4 8
24.2%
3 8
24.2%
2 8
24.2%

Most occurring characters

ValueCountFrequency (%)
1 9
27.3%
4 8
24.2%
3 8
24.2%
2 8
24.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9
27.3%
4 8
24.2%
3 8
24.2%
2 8
24.2%

Most occurring scripts

ValueCountFrequency (%)
Common 33
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9
27.3%
4 8
24.2%
3 8
24.2%
2 8
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9
27.3%
4 8
24.2%
3 8
24.2%
2 8
24.2%

Periodo
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size392.0 B
Ene-Mar 2022
 
1
Oct-Dic 2017
 
1
Abr-Jun 2014
 
1
Jul-Sept 2014
 
1
Oct-Dic 2014
 
1
Other values (28)
28 

Length

Max length13
Median length12
Mean length12.242424
Min length12

Characters and Unicode

Total characters404
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st rowEne-Mar 2022
2nd rowOct-Dic 2021
3rd rowJul-Sept 2021
4th rowAbr-Jun 2021
5th rowEne-Mar 2021

Common Values

ValueCountFrequency (%)
Ene-Mar 2022 1
 
3.0%
Oct-Dic 2017 1
 
3.0%
Abr-Jun 2014 1
 
3.0%
Jul-Sept 2014 1
 
3.0%
Oct-Dic 2014 1
 
3.0%
Ene-Mar 2015 1
 
3.0%
Abr-Jun 2015 1
 
3.0%
Jul-Sept 2015 1
 
3.0%
Oct-Dic 2015 1
 
3.0%
Ene-Mar 2016 1
 
3.0%
Other values (23) 23
69.7%

Length

2023-07-16T17:39:10.668703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ene-mar 9
13.6%
oct-dic 8
12.1%
abr-jun 8
12.1%
jul-sept 8
12.1%
2017 4
6.1%
2014 4
6.1%
2015 4
6.1%
2016 4
6.1%
2018 4
6.1%
2021 4
6.1%
Other values (3) 9
13.6%

Most occurring characters

ValueCountFrequency (%)
2 43
 
10.6%
0 37
 
9.2%
- 33
 
8.2%
33
 
8.2%
1 28
 
6.9%
e 17
 
4.2%
n 17
 
4.2%
r 17
 
4.2%
c 16
 
4.0%
t 16
 
4.0%
Other values (19) 147
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 140
34.7%
Decimal Number 132
32.7%
Uppercase Letter 66
16.3%
Dash Punctuation 33
 
8.2%
Space Separator 33
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17
12.1%
n 17
12.1%
r 17
12.1%
c 16
11.4%
t 16
11.4%
u 16
11.4%
a 9
6.4%
p 8
5.7%
l 8
5.7%
i 8
5.7%
Decimal Number
ValueCountFrequency (%)
2 43
32.6%
0 37
28.0%
1 28
21.2%
4 4
 
3.0%
7 4
 
3.0%
5 4
 
3.0%
6 4
 
3.0%
8 4
 
3.0%
9 4
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
J 16
24.2%
M 9
13.6%
E 9
13.6%
S 8
12.1%
A 8
12.1%
D 8
12.1%
O 8
12.1%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%
Space Separator
ValueCountFrequency (%)
33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 206
51.0%
Common 198
49.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17
 
8.3%
n 17
 
8.3%
r 17
 
8.3%
c 16
 
7.8%
t 16
 
7.8%
u 16
 
7.8%
J 16
 
7.8%
a 9
 
4.4%
M 9
 
4.4%
E 9
 
4.4%
Other values (8) 64
31.1%
Common
ValueCountFrequency (%)
2 43
21.7%
0 37
18.7%
- 33
16.7%
33
16.7%
1 28
14.1%
4 4
 
2.0%
7 4
 
2.0%
5 4
 
2.0%
6 4
 
2.0%
8 4
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 404
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 43
 
10.6%
0 37
 
9.2%
- 33
 
8.2%
33
 
8.2%
1 28
 
6.9%
e 17
 
4.2%
n 17
 
4.2%
r 17
 
4.2%
c 16
 
4.0%
t 16
 
4.0%
Other values (19) 147
36.4%

Accesos por cada 100 hogares
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.303939
Minimum49.55
Maximum73.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:10.831463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum49.55
5-th percentile50.244
Q152.63
median58.82
Q364.53
95-th percentile71.62
Maximum73.88
Range24.33
Interquartile range (IQR)11.9

Descriptive statistics

Standard deviation7.3154302
Coefficient of variation (CV)0.12335488
Kurtosis-1.043046
Mean59.303939
Median Absolute Deviation (MAD)6.19
Skewness0.38927497
Sum1957.03
Variance53.515518
MonotonicityNot monotonic
2023-07-16T17:39:11.013499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
73.88 1
 
3.0%
57.78 1
 
3.0%
49.86 1
 
3.0%
50.67 1
 
3.0%
50.5 1
 
3.0%
51.36 1
 
3.0%
51.76 1
 
3.0%
52.46 1
 
3.0%
52.63 1
 
3.0%
51.85 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
49.55 1
3.0%
49.86 1
3.0%
50.5 1
3.0%
50.67 1
3.0%
51.36 1
3.0%
51.76 1
3.0%
51.85 1
3.0%
52.46 1
3.0%
52.63 1
3.0%
53.34 1
3.0%
ValueCountFrequency (%)
73.88 1
3.0%
73.18 1
3.0%
70.58 1
3.0%
69.24 1
3.0%
67.95 1
3.0%
67.62 1
3.0%
66.31 1
3.0%
65.79 1
3.0%
64.53 1
3.0%
64.21 1
3.0%

Accesos por cada 100 hab
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.289394
Minimum15.05
Maximum23.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:11.193528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum15.05
5-th percentile15.298
Q116.12
median18.12
Q319.96
95-th percentile22.312
Maximum23.05
Range8
Interquartile range (IQR)3.84

Descriptive statistics

Standard deviation2.3919683
Coefficient of variation (CV)0.13078445
Kurtosis-1.0367955
Mean18.289394
Median Absolute Deviation (MAD)2
Skewness0.38171693
Sum603.55
Variance5.7215121
MonotonicityNot monotonic
2023-07-16T17:39:11.369359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
23.05 1
 
3.0%
17.79 1
 
3.0%
15.16 1
 
3.0%
15.43 1
 
3.0%
15.39 1
 
3.0%
15.68 1
 
3.0%
15.82 1
 
3.0%
16.05 1
 
3.0%
16.12 1
 
3.0%
15.9 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
15.05 1
3.0%
15.16 1
3.0%
15.39 1
3.0%
15.43 1
3.0%
15.68 1
3.0%
15.82 1
3.0%
15.9 1
3.0%
16.05 1
3.0%
16.12 1
3.0%
16.37 1
3.0%
ValueCountFrequency (%)
23.05 1
3.0%
22.81 1
3.0%
21.98 1
3.0%
21.55 1
3.0%
21.13 1
3.0%
21.01 1
3.0%
20.59 1
3.0%
20.36 1
3.0%
19.96 1
3.0%
19.92 1
3.0%

Banda ancha fija
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8108474.5
Minimum6362108
Maximum10611390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:11.562646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6362108
5-th percentile6506834.8
Q16952289
median8009981
Q39021040
95-th percentile10235884
Maximum10611390
Range4249282
Interquartile range (IQR)2068751

Descriptive statistics

Standard deviation1269430.9
Coefficient of variation (CV)0.15655607
Kurtosis-1.0492147
Mean8108474.5
Median Absolute Deviation (MAD)1057692
Skewness0.36318216
Sum2.6757966 × 108
Variance1.6114548 × 1012
MonotonicityNot monotonic
2023-07-16T17:39:11.726009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
10611390 1
 
3.0%
7842778 1
 
3.0%
6428329 1
 
3.0%
6559264 1
 
3.0%
6559172 1
 
3.0%
6699714 1
 
3.0%
6783279 1
 
3.0%
6902267 1
 
3.0%
6952289 1
 
3.0%
6874704 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
6362108 1
3.0%
6428329 1
3.0%
6559172 1
3.0%
6559264 1
3.0%
6699714 1
3.0%
6783279 1
3.0%
6874704 1
3.0%
6902267 1
3.0%
6952289 1
3.0%
7097604 1
3.0%
ValueCountFrequency (%)
10611390 1
3.0%
10476933 1
3.0%
10075184 1
3.0%
9852702 1
3.0%
9637956 1
3.0%
9561546 1
3.0%
9346183 1
3.0%
9142891 1
3.0%
9021040 1
3.0%
8938427 1
3.0%

Dial up
Real number (ℝ)

Distinct30
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20720.364
Minimum2853
Maximum39324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:11.909445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2853
5-th percentile2858.2
Q110016
median21812
Q332475
95-th percentile36890.6
Maximum39324
Range36471
Interquartile range (IQR)22459

Descriptive statistics

Standard deviation11717.209
Coefficient of variation (CV)0.56549244
Kurtosis-1.4297586
Mean20720.364
Median Absolute Deviation (MAD)11097
Skewness-0.068424529
Sum683772
Variance1.3729298 × 108
MonotonicityNot monotonic
2023-07-16T17:39:12.089840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
10016 4
 
12.1%
12619 1
 
3.0%
2853 1
 
3.0%
36139 1
 
3.0%
36007 1
 
3.0%
39324 1
 
3.0%
38018 1
 
3.0%
32909 1
 
3.0%
32801 1
 
3.0%
32542 1
 
3.0%
Other values (20) 20
60.6%
ValueCountFrequency (%)
2853 1
 
3.0%
2854 1
 
3.0%
2861 1
 
3.0%
3629 1
 
3.0%
9991 1
 
3.0%
10016 4
12.1%
10128 1
 
3.0%
10357 1
 
3.0%
10382 1
 
3.0%
12619 1
 
3.0%
ValueCountFrequency (%)
39324 1
3.0%
38018 1
3.0%
36139 1
3.0%
36007 1
3.0%
32909 1
3.0%
32801 1
3.0%
32652 1
3.0%
32542 1
3.0%
32475 1
3.0%
28545 1
3.0%

Total_BA
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8132521.3
Minimum6398398
Maximum10624009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:12.274026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6398398
5-th percentile6542949.8
Q16984831
median8037053
Q39031056
95-th percentile10247242
Maximum10624009
Range4225611
Interquartile range (IQR)2046225

Descriptive statistics

Standard deviation1260190.3
Coefficient of variation (CV)0.1549569
Kurtosis-1.0413158
Mean8132521.3
Median Absolute Deviation (MAD)1052222
Skewness0.36615189
Sum2.683732 × 108
Variance1.5880797 × 1012
MonotonicityNot monotonic
2023-07-16T17:39:12.443998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
10624009 1
 
3.0%
7870222 1
 
3.0%
6464468 1
 
3.0%
6595271 1
 
3.0%
6598496 1
 
3.0%
6737732 1
 
3.0%
6816188 1
 
3.0%
6935068 1
 
3.0%
6984831 1
 
3.0%
6907356 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
6398398 1
3.0%
6464468 1
3.0%
6595271 1
3.0%
6598496 1
3.0%
6737732 1
3.0%
6816188 1
3.0%
6907356 1
3.0%
6935068 1
3.0%
6984831 1
3.0%
7130079 1
3.0%
ValueCountFrequency (%)
10624009 1
3.0%
10489794 1
3.0%
10085541 1
3.0%
9863084 1
3.0%
9647972 1
3.0%
9571562 1
3.0%
9356199 1
3.0%
9164684 1
3.0%
9031056 1
3.0%
8960181 1
3.0%

ADSL
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3137917.6
Minimum1533240
Maximum3803024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:12.620558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1533240
5-th percentile1833424.6
Q12299457
median3574294
Q33723518
95-th percentile3790214.8
Maximum3803024
Range2269784
Interquartile range (IQR)1424061

Descriptive statistics

Standard deviation754929.95
Coefficient of variation (CV)0.2405831
Kurtosis-0.90859682
Mean3137917.6
Median Absolute Deviation (MAD)214402
Skewness-0.82798808
Sum1.0355128 × 108
Variance5.6991922 × 1011
MonotonicityNot monotonic
2023-07-16T17:39:12.786505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1533240 1
 
3.0%
3584311 1
 
3.0%
3708882 1
 
3.0%
3714764 1
 
3.0%
3764038 1
 
3.0%
3756153 1
 
3.0%
3767821 1
 
3.0%
3788696 1
 
3.0%
3803024 1
 
3.0%
3792493 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
1533240 1
3.0%
1657615 1
3.0%
1950631 1
3.0%
2018587 1
3.0%
2175211 1
3.0%
2213949 1
3.0%
2263889 1
3.0%
2295533 1
3.0%
2299457 1
3.0%
2414575 1
3.0%
ValueCountFrequency (%)
3803024 1
3.0%
3792493 1
3.0%
3788696 1
3.0%
3782085 1
3.0%
3776442 1
3.0%
3767821 1
3.0%
3764038 1
3.0%
3756153 1
3.0%
3723518 1
3.0%
3722794 1
3.0%

Cablemodem
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4008480.5
Minimum2407330
Maximum6073426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:12.964410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2407330
5-th percentile2506399.4
Q12898226
median3981129
Q34903674
95-th percentile5889450.2
Maximum6073426
Range3666096
Interquartile range (IQR)2005448

Descriptive statistics

Standard deviation1166214.8
Coefficient of variation (CV)0.29093688
Kurtosis-1.2856259
Mean4008480.5
Median Absolute Deviation (MAD)1023986
Skewness0.24339127
Sum1.3227986 × 108
Variance1.360057 × 1012
MonotonicityNot monotonic
2023-07-16T17:39:13.141950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
6073426 1
 
3.0%
3818157 1
 
3.0%
2461670 1
 
3.0%
2569868 1
 
3.0%
2536219 1
 
3.0%
2668248 1
 
3.0%
2756294 1
 
3.0%
2840203 1
 
3.0%
2898226 1
 
3.0%
2806359 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
2407330 1
3.0%
2461670 1
3.0%
2536219 1
3.0%
2569868 1
3.0%
2668248 1
3.0%
2756294 1
3.0%
2806359 1
3.0%
2840203 1
3.0%
2898226 1
3.0%
3035272 1
3.0%
ValueCountFrequency (%)
6073426 1
3.0%
5984240 1
3.0%
5826257 1
3.0%
5641731 1
3.0%
5424782 1
3.0%
5371824 1
3.0%
5259351 1
3.0%
5005115 1
3.0%
4903674 1
3.0%
4883869 1
3.0%

Fibra óptica
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean614314.24
Minimum139187
Maximum2219533
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:13.330524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum139187
5-th percentile149554.4
Q1167788
median217460
Q31047817
95-th percentile1768523.2
Maximum2219533
Range2080346
Interquartile range (IQR)880029

Descriptive statistics

Standard deviation612790.27
Coefficient of variation (CV)0.99751924
Kurtosis0.43721982
Mean614314.24
Median Absolute Deviation (MAD)67778
Skewness1.1985484
Sum20272370
Variance3.7551192 × 1011
MonotonicityNot monotonic
2023-07-16T17:39:13.498639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2219533 1
 
3.0%
211425 1
 
3.0%
149363 1
 
3.0%
155494 1
 
3.0%
149682 1
 
3.0%
168188 1
 
3.0%
150839 1
 
3.0%
162663 1
 
3.0%
139187 1
 
3.0%
164371 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
139187 1
3.0%
149363 1
3.0%
149682 1
3.0%
150323 1
3.0%
150839 1
3.0%
155494 1
3.0%
162663 1
3.0%
164371 1
3.0%
167788 1
3.0%
168188 1
3.0%
ValueCountFrequency (%)
2219533 1
3.0%
2072236 1
3.0%
1566048 1
3.0%
1472246 1
3.0%
1362976 1
3.0%
1311199 1
3.0%
1170879 1
3.0%
1106725 1
3.0%
1047817 1
3.0%
941295 1
3.0%

Wireless
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219996.7
Minimum1653
Maximum545814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:13.668070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1653
5-th percentile8503.4
Q182077
median155775
Q3352333
95-th percentile504691.8
Maximum545814
Range544161
Interquartile range (IQR)270256

Descriptive statistics

Standard deviation170710.29
Coefficient of variation (CV)0.77596749
Kurtosis-1.2133795
Mean219996.7
Median Absolute Deviation (MAD)108524
Skewness0.48245148
Sum7259891
Variance2.9142002 × 1010
MonotonicityNot monotonic
2023-07-16T17:39:13.847574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
545814 1
 
3.0%
155775 1
 
3.0%
72405 1
 
3.0%
85096 1
 
3.0%
76984 1
 
3.0%
79098 1
 
3.0%
82077 1
 
3.0%
8453 1
 
3.0%
85726 1
 
3.0%
8537 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
1653 1
3.0%
8453 1
3.0%
8537 1
3.0%
70749 1
3.0%
72405 1
3.0%
76984 1
3.0%
79098 1
3.0%
81455 1
3.0%
82077 1
3.0%
84813 1
3.0%
ValueCountFrequency (%)
545814 1
3.0%
523107 1
3.0%
492415 1
3.0%
476968 1
3.0%
434548 1
3.0%
421554 1
3.0%
413259 1
3.0%
376667 1
3.0%
352333 1
3.0%
340144 1
3.0%

Otros_tecno
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122928.85
Minimum543
Maximum265328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:14.019662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum543
5-th percentile8549.4
Q156122
median71573
Q3247016
95-th percentile257861.6
Maximum265328
Range264785
Interquartile range (IQR)190894

Descriptive statistics

Standard deviation96506.645
Coefficient of variation (CV)0.785061
Kurtosis-1.571631
Mean122928.85
Median Absolute Deviation (MAD)61686
Skewness0.39556874
Sum4056652
Variance9.3135325 × 109
MonotonicityNot monotonic
2023-07-16T17:39:14.198109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
251996 1
 
3.0%
100554 1
 
3.0%
72148 1
 
3.0%
70049 1
 
3.0%
71573 1
 
3.0%
66045 1
 
3.0%
59157 1
 
3.0%
58976 1
 
3.0%
58668 1
 
3.0%
58763 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
543 1
3.0%
7293 1
3.0%
9387 1
3.0%
9887 1
3.0%
13174 1
3.0%
25019 1
3.0%
55088 1
3.0%
55746 1
3.0%
56122 1
3.0%
58668 1
3.0%
ValueCountFrequency (%)
265328 1
3.0%
264326 1
3.0%
253552 1
3.0%
253036 1
3.0%
252596 1
3.0%
251996 1
3.0%
250455 1
3.0%
248821 1
3.0%
247016 1
3.0%
213298 1
3.0%

Total_tecno
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8132521.3
Minimum6398398
Maximum10624009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:14.376783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6398398
5-th percentile6542949.8
Q16984831
median8037053
Q39031056
95-th percentile10247242
Maximum10624009
Range4225611
Interquartile range (IQR)2046225

Descriptive statistics

Standard deviation1260190.3
Coefficient of variation (CV)0.1549569
Kurtosis-1.0413158
Mean8132521.3
Median Absolute Deviation (MAD)1052222
Skewness0.36615189
Sum2.683732 × 108
Variance1.5880797 × 1012
MonotonicityNot monotonic
2023-07-16T17:39:14.541030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
10624009 1
 
3.0%
7870222 1
 
3.0%
6464468 1
 
3.0%
6595271 1
 
3.0%
6598496 1
 
3.0%
6737732 1
 
3.0%
6816188 1
 
3.0%
6935068 1
 
3.0%
6984831 1
 
3.0%
6907356 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
6398398 1
3.0%
6464468 1
3.0%
6595271 1
3.0%
6598496 1
3.0%
6737732 1
3.0%
6816188 1
3.0%
6907356 1
3.0%
6935068 1
3.0%
6984831 1
3.0%
7130079 1
3.0%
ValueCountFrequency (%)
10624009 1
3.0%
10489794 1
3.0%
10085541 1
3.0%
9863084 1
3.0%
9647972 1
3.0%
9571562 1
3.0%
9356199 1
3.0%
9164684 1
3.0%
9031056 1
3.0%
8960181 1
3.0%

Mbps (Media de bajada)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.486364
Minimum3.62
Maximum55.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:14.703693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.62
5-th percentile3.826
Q15.08
median13.22
Q337.52
95-th percentile50.012
Maximum55.11
Range51.49
Interquartile range (IQR)32.44

Descriptive statistics

Standard deviation17.140551
Coefficient of variation (CV)0.87961771
Kurtosis-0.84765328
Mean19.486364
Median Absolute Deviation (MAD)8.67
Skewness0.82998432
Sum643.05
Variance293.79847
MonotonicityStrictly decreasing
2023-07-16T17:39:14.875849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
55.11 1
 
3.0%
12.01 1
 
3.0%
3.76 1
 
3.0%
3.87 1
 
3.0%
4.16 1
 
3.0%
4.35 1
 
3.0%
4.55 1
 
3.0%
4.79 1
 
3.0%
4.99 1
 
3.0%
5.08 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
3.62 1
3.0%
3.76 1
3.0%
3.87 1
3.0%
4.16 1
3.0%
4.35 1
3.0%
4.55 1
3.0%
4.79 1
3.0%
4.99 1
3.0%
5.08 1
3.0%
5.42 1
3.0%
ValueCountFrequency (%)
55.11 1
3.0%
52.34 1
3.0%
48.46 1
3.0%
45.63 1
3.0%
43.11 1
3.0%
42.36 1
3.0%
40.67 1
3.0%
38.32 1
3.0%
37.52 1
3.0%
28.26 1
3.0%

Hasta 512 kbps
Real number (ℝ)

Distinct32
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51088.424
Minimum5675
Maximum241713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:15.041529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5675
5-th percentile6774.2
Q122366
median39510
Q341262
95-th percentile181059.8
Maximum241713
Range236038
Interquartile range (IQR)18896

Descriptive statistics

Standard deviation54547.751
Coefficient of variation (CV)1.0677125
Kurtosis5.6636435
Mean51088.424
Median Absolute Deviation (MAD)4620
Skewness2.486798
Sum1685918
Variance2.9754571 × 109
MonotonicityNot monotonic
2023-07-16T17:39:15.215254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
41038 2
 
6.1%
34890 1
 
3.0%
19022 1
 
3.0%
41064 1
 
3.0%
37430 1
 
3.0%
44075 1
 
3.0%
41158 1
 
3.0%
40723 1
 
3.0%
35030 1
 
3.0%
34243 1
 
3.0%
Other values (22) 22
66.7%
ValueCountFrequency (%)
5675 1
3.0%
5972 1
3.0%
7309 1
3.0%
15041 1
3.0%
19022 1
3.0%
20104 1
3.0%
20653 1
3.0%
20724 1
3.0%
22366 1
3.0%
30428 1
3.0%
ValueCountFrequency (%)
241713 1
3.0%
202790 1
3.0%
166573 1
3.0%
138740 1
3.0%
52684 1
3.0%
44075 1
3.0%
44008 1
3.0%
42550 1
3.0%
41262 1
3.0%
41158 1
3.0%

Entre 512 Kbps y 1 Mbps
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252235.33
Minimum28521
Maximum687619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:15.389800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum28521
5-th percentile36454.2
Q156170
median219467
Q3404810
95-th percentile627374
Maximum687619
Range659098
Interquartile range (IQR)348640

Descriptive statistics

Standard deviation202020.77
Coefficient of variation (CV)0.80092178
Kurtosis-0.60805878
Mean252235.33
Median Absolute Deviation (MAD)170777
Skewness0.71654146
Sum8323766
Variance4.0812392 × 1010
MonotonicityNot monotonic
2023-07-16T17:39:15.560805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
104840 1
 
3.0%
221474 1
 
3.0%
656408 1
 
3.0%
608018 1
 
3.0%
554749 1
 
3.0%
516919 1
 
3.0%
500175 1
 
3.0%
455777 1
 
3.0%
427394 1
 
3.0%
404810 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
28521 1
3.0%
28980 1
3.0%
41437 1
3.0%
41674 1
3.0%
42024 1
3.0%
42185 1
3.0%
44005 1
3.0%
48690 1
3.0%
56170 1
3.0%
80599 1
3.0%
ValueCountFrequency (%)
687619 1
3.0%
656408 1
3.0%
608018 1
3.0%
554749 1
3.0%
516919 1
3.0%
500175 1
3.0%
455777 1
3.0%
427394 1
3.0%
404810 1
3.0%
384221 1
3.0%

Entre 1 Mbps y 6 Mbps
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3771890.1
Minimum1263273
Maximum5153437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:15.739569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1263273
5-th percentile2084045.8
Q12651502
median3444458
Q35049640
95-th percentile5142888.2
Maximum5153437
Range3890164
Interquartile range (IQR)2398138

Descriptive statistics

Standard deviation1173775.6
Coefficient of variation (CV)0.31119029
Kurtosis-0.96018962
Mean3771890.1
Median Absolute Deviation (MAD)913187
Skewness-0.32287686
Sum1.2447237 × 108
Variance1.3777491 × 1012
MonotonicityNot monotonic
2023-07-16T17:39:15.911247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1263273 1
 
3.0%
3773159 1
 
3.0%
5149574 1
 
3.0%
5153437 1
 
3.0%
5084556 1
 
3.0%
5121423 1
 
3.0%
5138431 1
 
3.0%
5087802 1
 
3.0%
5049640 1
 
3.0%
4944358 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
1263273 1
3.0%
1413208 1
3.0%
2531271 1
3.0%
2550229 1
3.0%
2593477 1
3.0%
2622638 1
3.0%
2637984 1
3.0%
2649819 1
3.0%
2651502 1
3.0%
2792684 1
3.0%
ValueCountFrequency (%)
5153437 1
3.0%
5149574 1
3.0%
5138431 1
3.0%
5130294 1
3.0%
5121423 1
3.0%
5087802 1
3.0%
5084556 1
3.0%
5058481 1
3.0%
5049640 1
3.0%
4944358 1
3.0%

Entre 6 Mbps y 10 Mbps
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean880732.52
Minimum289182
Maximum1245333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:16.096915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum289182
5-th percentile360698.8
Q1762999
median975027
Q31046128
95-th percentile1141122.4
Maximum1245333
Range956151
Interquartile range (IQR)283129

Descriptive statistics

Standard deviation243586.35
Coefficient of variation (CV)0.27657245
Kurtosis0.44239528
Mean880732.52
Median Absolute Deviation (MAD)98848
Skewness-1.051209
Sum29064173
Variance5.933431 × 1010
MonotonicityNot monotonic
2023-07-16T17:39:16.408704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1209148 1
 
3.0%
1005545 1
 
3.0%
341689 1
 
3.0%
373372 1
 
3.0%
496251 1
 
3.0%
571620 1
 
3.0%
645440 1
 
3.0%
701187 1
 
3.0%
726740 1
 
3.0%
762999 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
289182 1
3.0%
341689 1
3.0%
373372 1
3.0%
496251 1
3.0%
571620 1
3.0%
645440 1
3.0%
701187 1
3.0%
726740 1
3.0%
762999 1
3.0%
796998 1
3.0%
ValueCountFrequency (%)
1245333 1
3.0%
1209148 1
3.0%
1095772 1
3.0%
1080279 1
3.0%
1073875 1
3.0%
1072722 1
3.0%
1062810 1
3.0%
1053107 1
3.0%
1046128 1
3.0%
1040017 1
3.0%

Entre 10 Mbps y 20 Mbps
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean923801.21
Minimum101127
Maximum2068087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:16.580604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum101127
5-th percentile168399
Q1641646
median807775
Q31169632
95-th percentile1800547.4
Maximum2068087
Range1966960
Interquartile range (IQR)527986

Descriptive statistics

Standard deviation502754.42
Coefficient of variation (CV)0.5442236
Kurtosis-0.22995988
Mean923801.21
Median Absolute Deviation (MAD)268361
Skewness0.48535585
Sum30485440
Variance2.5276201 × 1011
MonotonicityNot monotonic
2023-07-16T17:39:16.759011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
967508 1
 
3.0%
1730353 1
 
3.0%
147273 1
 
3.0%
182483 1
 
3.0%
276254 1
 
3.0%
348102 1
 
3.0%
432762 1
 
3.0%
539414 1
 
3.0%
639011 1
 
3.0%
641646 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
101127 1
3.0%
147273 1
3.0%
182483 1
3.0%
276254 1
3.0%
348102 1
3.0%
432762 1
3.0%
539414 1
3.0%
639011 1
3.0%
641646 1
3.0%
693277 1
3.0%
ValueCountFrequency (%)
2068087 1
3.0%
1905839 1
3.0%
1730353 1
3.0%
1607137 1
3.0%
1592304 1
3.0%
1472634 1
3.0%
1464748 1
3.0%
1342000 1
3.0%
1169632 1
3.0%
1129987 1
3.0%

Entre 20 Mbps y 30 Mbps
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean505049.94
Minimum345
Maximum1690612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:16.948704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum345
5-th percentile495.4
Q127664
median523437
Q3647401
95-th percentile1378896.4
Maximum1690612
Range1690267
Interquartile range (IQR)619737

Descriptive statistics

Standard deviation486393.95
Coefficient of variation (CV)0.9630611
Kurtosis-0.064741811
Mean505049.94
Median Absolute Deviation (MAD)449460
Skewness0.84752182
Sum16666648
Variance2.3657908 × 1011
MonotonicityNot monotonic
2023-07-16T17:39:17.124523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
509830 1
 
3.0%
631946 1
 
3.0%
478 1
 
3.0%
507 1
 
3.0%
4371 1
 
3.0%
7643 1
 
3.0%
10045 1
 
3.0%
13101 1
 
3.0%
17568 1
 
3.0%
27664 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
345 1
3.0%
478 1
3.0%
507 1
3.0%
4371 1
3.0%
7643 1
3.0%
10045 1
3.0%
13101 1
3.0%
17568 1
3.0%
27664 1
3.0%
73977 1
3.0%
ValueCountFrequency (%)
1690612 1
3.0%
1571692 1
3.0%
1250366 1
3.0%
1244215 1
3.0%
1141545 1
3.0%
1004083 1
3.0%
1000036 1
3.0%
862010 1
3.0%
647401 1
3.0%
631946 1
3.0%

Más de 30 Mbps
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1599202
Minimum11595
Maximum6336187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:17.309935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11595
5-th percentile12358
Q123380
median397977
Q33500882
95-th percentile5381833.2
Maximum6336187
Range6324592
Interquartile range (IQR)3477502

Descriptive statistics

Standard deviation2066393.3
Coefficient of variation (CV)1.2921402
Kurtosis-0.44739407
Mean1599202
Median Absolute Deviation (MAD)382748
Skewness1.0123874
Sum52773667
Variance4.2699811 × 1012
MonotonicityStrictly decreasing
2023-07-16T17:39:17.483281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
6336187 1
 
3.0%
337559 1
 
3.0%
12259 1
 
3.0%
12424 1
 
3.0%
15229 1
 
3.0%
16347 1
 
3.0%
18529 1
 
3.0%
20677 1
 
3.0%
22170 1
 
3.0%
23380 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
11595 1
3.0%
12259 1
3.0%
12424 1
3.0%
15229 1
3.0%
16347 1
3.0%
18529 1
3.0%
20677 1
3.0%
22170 1
3.0%
23380 1
3.0%
29020 1
3.0%
ValueCountFrequency (%)
6336187 1
3.0%
6032322 1
3.0%
4948174 1
3.0%
4661291 1
3.0%
4379965 1
3.0%
4239237 1
3.0%
4053461 1
3.0%
3711499 1
3.0%
3500882 1
3.0%
2831253 1
3.0%

Otros_velo_rango
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103903
Minimum0
Maximum247983
Zeros15
Zeros (%)45.5%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:17.649395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median138840
Q3194212
95-th percentile240459.8
Maximum247983
Range247983
Interquartile range (IQR)194212

Descriptive statistics

Standard deviation99722.046
Coefficient of variation (CV)0.95976099
Kurtosis-1.8590718
Mean103903
Median Absolute Deviation (MAD)104627
Skewness0.014638615
Sum3428799
Variance9.9444864 × 109
MonotonicityNot monotonic
2023-07-16T17:39:17.815836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 15
45.5%
243467 1
 
3.0%
155145 1
 
3.0%
236584 1
 
3.0%
238455 1
 
3.0%
156475 1
 
3.0%
138840 1
 
3.0%
126570 1
 
3.0%
247983 1
 
3.0%
198333 1
 
3.0%
Other values (9) 9
27.3%
ValueCountFrequency (%)
0 15
45.5%
126570 1
 
3.0%
138840 1
 
3.0%
155145 1
 
3.0%
156475 1
 
3.0%
163269 1
 
3.0%
167369 1
 
3.0%
185102 1
 
3.0%
186797 1
 
3.0%
194008 1
 
3.0%
ValueCountFrequency (%)
247983 1
3.0%
243467 1
3.0%
238455 1
3.0%
236584 1
3.0%
201777 1
3.0%
200162 1
3.0%
198333 1
3.0%
194251 1
3.0%
194212 1
3.0%
194008 1
3.0%

Total_velo_rango
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8087902.6
Minimum6272846
Maximum10624009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:17.988655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6272846
5-th percentile6360100.6
Q16916766
median8037053
Q39031056
95-th percentile10247242
Maximum10624009
Range4351163
Interquartile range (IQR)2114290

Descriptive statistics

Standard deviation1304616.6
Coefficient of variation (CV)0.16130469
Kurtosis-1.0877651
Mean8087902.6
Median Absolute Deviation (MAD)1120287
Skewness0.32180086
Sum2.6690078 × 108
Variance1.7020246 × 1012
MonotonicityNot monotonic
2023-07-16T17:39:18.154978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
10624009 1
 
3.0%
7870222 1
 
3.0%
6348745 1
 
3.0%
6367671 1
 
3.0%
6475485 1
 
3.0%
6623212 1
 
3.0%
6786105 1
 
3.0%
6852988 1
 
3.0%
6916766 1
 
3.0%
6835285 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
6272846 1
3.0%
6348745 1
3.0%
6367671 1
3.0%
6475485 1
3.0%
6623212 1
3.0%
6786105 1
3.0%
6835285 1
3.0%
6852988 1
3.0%
6916766 1
3.0%
7141925 1
3.0%
ValueCountFrequency (%)
10624009 1
3.0%
10489794 1
3.0%
10085541 1
3.0%
9863084 1
3.0%
9647972 1
3.0%
9571562 1
3.0%
9356199 1
3.0%
9164684 1
3.0%
9031056 1
3.0%
8960181 1
3.0%

Ingresos (miles de pesos)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17849908
Minimum2984054
Maximum51432896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size392.0 B
2023-07-16T17:39:18.325394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2984054
5-th percentile3395509.2
Q15936845
median13171459
Q329946216
95-th percentile43987121
Maximum51432896
Range48448842
Interquartile range (IQR)24009371

Descriptive statistics

Standard deviation14188848
Coefficient of variation (CV)0.79489753
Kurtosis-0.43816958
Mean17849908
Median Absolute Deviation (MAD)8295074
Skewness0.85505772
Sum5.8904697 × 108
Variance2.0132341 × 1014
MonotonicityNot monotonic
2023-07-16T17:39:18.499539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
51432896 1
 
3.0%
11226127 1
 
3.0%
3270816 1
 
3.0%
3478638 1
 
3.0%
3950441 1
 
3.0%
4876385 1
 
3.0%
4701791 1
 
3.0%
5153739 1
 
3.0%
5376899 1
 
3.0%
5936845 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
2984054 1
3.0%
3270816 1
3.0%
3478638 1
3.0%
3950441 1
3.0%
4701791 1
3.0%
4876385 1
3.0%
5153739 1
3.0%
5376899 1
3.0%
5936845 1
3.0%
6534241 1
3.0%
ValueCountFrequency (%)
51432896 1
3.0%
45467887 1
3.0%
42999944 1
3.0%
38239667 1
3.0%
36676371 1
3.0%
33539703 1
3.0%
32102476 1
3.0%
31997445 1
3.0%
29946216 1
3.0%
24169251 1
3.0%

Interactions

2023-07-16T17:39:05.784101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:45.445740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:50.140201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:53.739476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:57.252154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:00.576044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:03.903995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:07.149992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:10.592245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:14.310050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:18.126564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:21.642342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:25.436565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:29.108582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:32.841702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:36.450101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:39.953692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:43.646976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:46.983999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:50.566859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:54.902709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:58.775851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:39:02.667455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:39:05.938813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:45.627918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:50.354523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:53.900790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:57.483572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:00.736156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:04.091502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:07.298562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:10.759453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:14.486833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:18.313203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:21.800833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:25.597457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:29.264707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:33.022257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:36.607535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:40.114740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:43.847734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:47.144470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:50.740784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:55.071846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:58.961484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:39:02.819193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:39:06.072432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:45.816226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:50.557282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:54.050342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:57.632369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:00.887764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:04.224412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:07.524314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:10.918974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:14.638197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:18.478047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:21.958592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:25.740774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:29.430870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:33.173873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:36.760331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:40.260079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:44.052939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:47.300531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:50.895635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:55.220689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:59.157842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:39:02.956633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:39:06.225727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:46.110262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:50.774729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:54.214206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:57.785138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:01.049962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:04.370119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:07.682993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:11.091458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:14.804744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:18.652123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:22.178395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:25.912647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-16T17:39:05.403274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:39:08.711327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:49.848441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:53.472647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:56.946481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:00.319261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:03.630300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:06.904460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:10.302168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:14.005700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:17.814335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:21.362655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:25.001135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:28.852579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:32.464051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:36.191125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:39.679219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:43.178370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:46.727585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:50.274321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:54.596295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:58.492994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:39:02.397066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:39:05.532576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:39:08.843713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:49.991994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:53.603165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:37:57.083351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:00.444666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:03.764812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:07.023100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:10.431458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:14.144094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:17.953246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:21.497111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:25.293606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:28.980784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:32.651910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:36.316627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:39.811460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:43.320198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:46.852056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:50.420112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:54.753372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:38:58.637225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:39:02.527861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T17:39:05.655721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-16T17:39:18.686482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AñoAccesos por cada 100 hogaresAccesos por cada 100 habBanda ancha fijaDial upTotal_BAADSLCablemodemFibra ópticaWirelessOtros_tecnoTotal_tecnoMbps (Media de bajada)Hasta 512 kbpsEntre 512 Kbps y 1 MbpsEntre 1 Mbps y 6 MbpsEntre 6 Mbps y 10 MbpsEntre 10 Mbps y 20 MbpsEntre 20 Mbps y 30 MbpsMás de 30 MbpsOtros_velo_rangoTotal_velo_rangoIngresos (miles de pesos)TrimestrePeriodo
Año1.0000.9840.9840.987-0.5910.987-0.9020.9910.9840.8990.5910.9870.9930.086-0.954-0.9740.8960.4150.7220.9930.7880.9870.9930.0001.000
Accesos por cada 100 hogares0.9841.0001.0000.999-0.5520.999-0.8980.9950.9770.9190.6180.9990.9930.091-0.957-0.9640.8960.4260.7510.9930.8010.9980.9920.0001.000
Accesos por cada 100 hab0.9841.0001.0000.999-0.5520.999-0.8980.9950.9770.9190.6180.9990.9930.091-0.957-0.9640.8960.4260.7510.9930.8010.9980.9920.0001.000
Banda ancha fija0.9870.9990.9991.000-0.5561.000-0.8990.9960.9790.9210.6151.0000.9950.091-0.956-0.9680.8990.4230.7430.9950.7970.9990.9940.0001.000
Dial up-0.591-0.552-0.552-0.5561.000-0.5550.528-0.569-0.580-0.506-0.142-0.555-0.568-0.0020.5720.575-0.577-0.292-0.355-0.568-0.377-0.547-0.5670.0001.000
Total_BA0.9870.9990.9991.000-0.5551.000-0.8980.9960.9780.9190.6151.0000.9950.096-0.956-0.9700.9000.4240.7440.9950.7970.9990.9940.0001.000
ADSL-0.902-0.898-0.898-0.8990.528-0.8981.000-0.903-0.931-0.877-0.633-0.898-0.902-0.2190.8660.893-0.789-0.269-0.633-0.902-0.786-0.896-0.9020.0001.000
Cablemodem0.9910.9950.9950.996-0.5690.996-0.9031.0000.9820.9240.6000.9960.9990.071-0.963-0.9760.9080.4210.7300.9990.7860.9950.9980.0001.000
Fibra óptica0.9840.9770.9770.979-0.5800.978-0.9310.9821.0000.9030.6020.9780.9840.104-0.948-0.9630.8940.4050.7150.9840.7860.9760.9850.0001.000
Wireless0.8990.9190.9190.921-0.5060.919-0.8770.9240.9031.0000.6710.9190.9190.203-0.874-0.8630.8240.2530.6050.9190.6660.9210.9180.0001.000
Otros_tecno0.5910.6180.6180.615-0.1420.615-0.6330.6000.6020.6711.0000.6150.6010.508-0.584-0.5370.443-0.0110.4450.6010.6460.6220.6010.0001.000
Total_tecno0.9870.9990.9991.000-0.5551.000-0.8980.9960.9780.9190.6151.0000.9950.096-0.956-0.9700.9000.4240.7440.9950.7970.9990.9940.0001.000
Mbps (Media de bajada)0.9930.9930.9930.995-0.5680.995-0.9020.9990.9840.9190.6010.9951.0000.075-0.964-0.9800.9100.4220.7311.0000.7860.9940.9990.0001.000
Hasta 512 kbps0.0860.0910.0910.091-0.0020.096-0.2190.0710.1040.2030.5080.0960.0751.000-0.104-0.001-0.141-0.4050.0030.0750.1330.1090.0760.0001.000
Entre 512 Kbps y 1 Mbps-0.954-0.957-0.957-0.9560.572-0.9560.866-0.963-0.948-0.874-0.584-0.956-0.964-0.1041.0000.945-0.896-0.418-0.757-0.964-0.785-0.955-0.9640.0001.000
Entre 1 Mbps y 6 Mbps-0.974-0.964-0.964-0.9680.575-0.9700.893-0.976-0.963-0.863-0.537-0.970-0.980-0.0010.9451.000-0.915-0.438-0.715-0.980-0.796-0.967-0.9800.0001.000
Entre 6 Mbps y 10 Mbps0.8960.8960.8960.899-0.5770.900-0.7890.9080.8940.8240.4430.9000.910-0.141-0.896-0.9151.0000.4360.5610.9100.6250.8960.9100.0001.000
Entre 10 Mbps y 20 Mbps0.4150.4260.4260.423-0.2920.424-0.2690.4210.4050.253-0.0110.4240.422-0.405-0.418-0.4380.4361.0000.7250.4220.4340.4180.4230.0001.000
Entre 20 Mbps y 30 Mbps0.7220.7510.7510.743-0.3550.744-0.6330.7300.7150.6050.4450.7440.7310.003-0.757-0.7150.5610.7251.0000.7310.7690.7430.7300.0001.000
Más de 30 Mbps0.9930.9930.9930.995-0.5680.995-0.9020.9990.9840.9190.6010.9951.0000.075-0.964-0.9800.9100.4220.7311.0000.7860.9940.9990.0001.000
Otros_velo_rango0.7880.8010.8010.797-0.3770.797-0.7860.7860.7860.6660.6460.7970.7860.133-0.785-0.7960.6250.4340.7690.7861.0000.7970.7840.0001.000
Total_velo_rango0.9870.9980.9980.999-0.5470.999-0.8960.9950.9760.9210.6220.9990.9940.109-0.955-0.9670.8960.4180.7430.9940.7971.0000.9930.0001.000
Ingresos (miles de pesos)0.9930.9920.9920.994-0.5670.994-0.9020.9980.9850.9180.6010.9940.9990.076-0.964-0.9800.9100.4230.7300.9990.7840.9931.0000.0001.000
Trimestre0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.000
Periodo1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-07-16T17:39:09.084822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-16T17:39:09.594471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AñoTrimestrePeriodoAccesos por cada 100 hogaresAccesos por cada 100 habBanda ancha fijaDial upTotal_BAADSLCablemodemFibra ópticaWirelessOtros_tecnoTotal_tecnoMbps (Media de bajada)Hasta 512 kbpsEntre 512 Kbps y 1 MbpsEntre 1 Mbps y 6 MbpsEntre 6 Mbps y 10 MbpsEntre 10 Mbps y 20 MbpsEntre 20 Mbps y 30 MbpsMás de 30 MbpsOtros_velo_rangoTotal_velo_rangoIngresos (miles de pesos)
020221Ene-Mar 202273.8823.051061139012619106240091533240607342622195335458142519961062400955.1134890.0104840.01263273.01209148.0967508.0509830.06336187.0198333.010624009.051432896
120214Oct-Dic 202173.1822.811047693312861104897941657615598424020722365231072525961048979452.3441262.028521.01413208.01245333.0976539.0558358.06032322.0194251.010489794.045467887
220213Jul-Sept 202170.5821.98100751841035710085541195063158262571566048492415250191008554148.4640174.041437.02550229.01095772.0710122.0536364.04948174.0163269.010085541.042999944
320212Abr-Jun 202169.2421.559852702103829863084201858756417311472246476968253552986308445.6340172.042024.02531271.01080279.0693277.0647401.04661291.0167369.09863084.038239667
420211Ene-Mar 202167.9521.139637956100169647972217521154247821362976434548250455964797243.1139487.041674.02593477.01072722.0737930.0595920.04379965.0186797.09647972.036676371
520204Oct-Dic 202067.6221.019561546100169571562221394953718241311199421554253036957156242.3639510.042185.02622638.01073875.0786595.0582420.04239237.0185102.09571562.033539703
620203Jul-Sept 202066.3120.599346183100169356199226388952593511170879413259248821935619940.6741038.044005.02637984.01040017.0799350.0538567.04053461.0201777.09356199.031997445
720202Abr-Jun 202064.2119.929021040100169031056229553350051151106725376667247016903105638.3241038.048690.02651502.01053107.0807775.0523437.03711499.0194008.09031056.032102476
820201Ene-Mar 202062.8619.48880243599918812426229945749036741047817352333209145881242637.5242550.056170.02649819.01022014.0814470.0532309.03500882.0194212.08812426.029946216
920194Oct-Dic 201962.9219.49878305310128879318124145754883869941295340144213298879318128.2638272.028980.02792684.01046128.0851619.01004083.02831253.0200162.08793181.024169251
AñoTrimestrePeriodoAccesos por cada 100 hogaresAccesos por cada 100 habBanda ancha fijaDial upTotal_BAADSLCablemodemFibra ópticaWirelessOtros_tecnoTotal_tecnoMbps (Media de bajada)Hasta 512 kbpsEntre 512 Kbps y 1 MbpsEntre 1 Mbps y 6 MbpsEntre 6 Mbps y 10 MbpsEntre 10 Mbps y 20 MbpsEntre 20 Mbps y 30 MbpsMás de 30 MbpsOtros_velo_rangoTotal_velo_rangoIngresos (miles de pesos)
2320162Abr-Jun 201653.3416.37709760432475713007937820853035272167788854525948271300795.4244008.0384221.05058481.0796998.0785759.073977.029020.00.07172464.06534241
2420161Ene-Mar 201651.8515.9068747043265269073563792493280635916437185375876369073565.0830428.0404810.04944358.0762999.0641646.027664.023380.00.06835285.05936845
2520154Oct-Dic 201552.6316.12695228932542698483138030242898226139187857265866869848314.9934243.0427394.05049640.0726740.0639011.017568.022170.00.06916766.05376899
2620153Jul-Sept 201552.4616.0569022673280169350683788696284020316266384535897669350684.7935030.0455777.05087802.0701187.0539414.013101.020677.00.06852988.05153739
2720152Abr-Jun 201551.7615.82678327932909681618837678212756294150839820775915768161884.5540723.0500175.05138431.0645440.0432762.010045.018529.00.06786105.04701791
2820151Ene-Mar 201551.3615.68669971438018673773237561532668248168188790986604567377324.3541158.0516919.05121423.0571620.0348102.07643.016347.00.06623212.04876385
2920144Oct-Dic 201450.5015.39655917239324659849637640382536219149682769847157365984964.1644075.0554749.05084556.0496251.0276254.04371.015229.00.06475485.03950441
3020143Jul-Sept 201450.6715.43655926436007659527137147642569868155494850967004965952713.8737430.0608018.05153437.0373372.0182483.0507.012424.00.06367671.03478638
3120142Abr-Jun 201449.8615.16642832936139646446837088822461670149363724057214864644683.7641064.0656408.05149574.0341689.0147273.0478.012259.00.06348745.03270816
3220141Ene-Mar 201449.5515.056362108362963983983697066240733015032370749729363983983.6252684.0687619.05130294.0289182.0101127.0345.011595.00.06272846.02984054